Monitoring vegetation dynamics in Qinling–Daba mountains during 2001–2023 using an improved two-leaf model and remote-sensing datasets
Mountains are critical in terrestrial ecosystems, understanding vegetation dynamics is essential in the context of global climate change. This study employed an improved two-leaf light use efficiency (LUE) model to simulate gross primary productivity (GPP), integrating the leaf area index (LAI) and...
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| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2538238 |
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| author | Enjun Gong Jing Zhang Jun Wang |
| author_facet | Enjun Gong Jing Zhang Jun Wang |
| author_sort | Enjun Gong |
| collection | DOAJ |
| description | Mountains are critical in terrestrial ecosystems, understanding vegetation dynamics is essential in the context of global climate change. This study employed an improved two-leaf light use efficiency (LUE) model to simulate gross primary productivity (GPP), integrating the leaf area index (LAI) and kernel normalized difference vegetation index (kNDVI). We analyzed the vegetation dynamics and inter-relationships between these indices in the Qinling – Daba Mountains during 2001–2023. The results indicate the following: The root mean square error of the improved model decreased by 19.3%, significantly enhancing model accuracy. Throughout the study period, the three vegetation indices demonstrated a high degree of consistency, exhibiting a general upward trend. Notably, 77.5% of the area experienced synchronous increases, primarily concentrated in the Qinling – Daba Mountains, while only 2.2% of the area showed synchronous decreases, scattered across the area. LAI exhibited a stronger correlation and had a greater influence on GPP than kNDVI, indicating that the vegetation structure is more critical in mountain productivity than greenness. However, in specific regions, such as the Hubei Province, the influence of kNDVI was more significant. In addition, factors such as elevation, precipitation, and temperature were found to be important drivers of vegetation changes. |
| format | Article |
| id | doaj-art-4faec76623ec400ea50f656eed42f77f |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-4faec76623ec400ea50f656eed42f77f2025-08-25T11:31:51ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2538238Monitoring vegetation dynamics in Qinling–Daba mountains during 2001–2023 using an improved two-leaf model and remote-sensing datasetsEnjun Gong0Jing Zhang1Jun Wang2Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an, People’s Republic of ChinaSchool of Geological Engineering and Geomatics, Chang’an University, Xi’an, People’s Republic of ChinaShaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an, People’s Republic of ChinaMountains are critical in terrestrial ecosystems, understanding vegetation dynamics is essential in the context of global climate change. This study employed an improved two-leaf light use efficiency (LUE) model to simulate gross primary productivity (GPP), integrating the leaf area index (LAI) and kernel normalized difference vegetation index (kNDVI). We analyzed the vegetation dynamics and inter-relationships between these indices in the Qinling – Daba Mountains during 2001–2023. The results indicate the following: The root mean square error of the improved model decreased by 19.3%, significantly enhancing model accuracy. Throughout the study period, the three vegetation indices demonstrated a high degree of consistency, exhibiting a general upward trend. Notably, 77.5% of the area experienced synchronous increases, primarily concentrated in the Qinling – Daba Mountains, while only 2.2% of the area showed synchronous decreases, scattered across the area. LAI exhibited a stronger correlation and had a greater influence on GPP than kNDVI, indicating that the vegetation structure is more critical in mountain productivity than greenness. However, in specific regions, such as the Hubei Province, the influence of kNDVI was more significant. In addition, factors such as elevation, precipitation, and temperature were found to be important drivers of vegetation changes.https://www.tandfonline.com/doi/10.1080/17538947.2025.2538238Gross primary productivity (GPP)leaf area index (LAI)kernel normalized difference vegetation index (kNDVI)Pearson correlationrandom forest model |
| spellingShingle | Enjun Gong Jing Zhang Jun Wang Monitoring vegetation dynamics in Qinling–Daba mountains during 2001–2023 using an improved two-leaf model and remote-sensing datasets International Journal of Digital Earth Gross primary productivity (GPP) leaf area index (LAI) kernel normalized difference vegetation index (kNDVI) Pearson correlation random forest model |
| title | Monitoring vegetation dynamics in Qinling–Daba mountains during 2001–2023 using an improved two-leaf model and remote-sensing datasets |
| title_full | Monitoring vegetation dynamics in Qinling–Daba mountains during 2001–2023 using an improved two-leaf model and remote-sensing datasets |
| title_fullStr | Monitoring vegetation dynamics in Qinling–Daba mountains during 2001–2023 using an improved two-leaf model and remote-sensing datasets |
| title_full_unstemmed | Monitoring vegetation dynamics in Qinling–Daba mountains during 2001–2023 using an improved two-leaf model and remote-sensing datasets |
| title_short | Monitoring vegetation dynamics in Qinling–Daba mountains during 2001–2023 using an improved two-leaf model and remote-sensing datasets |
| title_sort | monitoring vegetation dynamics in qinling daba mountains during 2001 2023 using an improved two leaf model and remote sensing datasets |
| topic | Gross primary productivity (GPP) leaf area index (LAI) kernel normalized difference vegetation index (kNDVI) Pearson correlation random forest model |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2538238 |
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